--- base_model: klue/roberta-base library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 케라스타즈 엘릭서 얼팀 헤어오일 엠페리얼 티 100ml × 1개 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어오일 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어오일 - text: 쿤달 네이처 샴푸 싱글파우치 베이비파우더향 10ml × 100개입 Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸;(#M)쿠팡 홈>생활용품>헤어/바디/세안>샴푸/린스>샴푸>일반샴푸 Coupang > 뷰티 > 헤어 > 샴푸 > 일반샴푸 - text: 려 자양 탈모전문케어 트리트먼트 경주달밤/여수하늘 200ml 옵션 229318 02 여수하늘 200ml (#M)11st>남성화장품>남성에센스>남성에센스 11st > 뷰티 > 남성화장품 > 남성에센스 - text: '[미쟝센] 퍼펙트세럼 모음 80ml 2입 04 로즈퍼퓸 세럼 2개 LotteOn > 뷰티 > 헤어케어 > 헤어에센스 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일' - text: 미쟝센 퍼펙트세럼 헤어에센스 오리지날 스타일링 슈퍼리치 미쟝센 NEW퍼펙트세럼80ml 코코워터 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일 inference: true model-index: - name: SetFit with klue/roberta-base results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.7773372337596403 name: Accuracy --- # SetFit with klue/roberta-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 10 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 9 | | | 2 | | | 0 | | | 4 | | | 8 | | | 6 | | | 3 | | | 5 | | | 7 | | | 1 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.7773 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_item_top_bt13") # Run inference preds = model("케라스타즈 엘릭서 얼팀 헤어오일 엠페리얼 티 100ml × 1개 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어오일 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어오일") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 13 | 24.996 | 125 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | | 2 | 50 | | 3 | 50 | | 4 | 50 | | 5 | 50 | | 6 | 50 | | 7 | 50 | | 8 | 50 | | 9 | 50 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (30, 30) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 100 - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:-----:|:-------------:|:---------------:| | 0.0013 | 1 | 0.4713 | - | | 0.0639 | 50 | 0.4253 | - | | 0.1279 | 100 | 0.3864 | - | | 0.1918 | 150 | 0.358 | - | | 0.2558 | 200 | 0.3284 | - | | 0.3197 | 250 | 0.3139 | - | | 0.3836 | 300 | 0.2877 | - | | 0.4476 | 350 | 0.2604 | - | | 0.5115 | 400 | 0.2218 | - | | 0.5754 | 450 | 0.1841 | - | | 0.6394 | 500 | 0.1548 | - | | 0.7033 | 550 | 0.1272 | - | | 0.7673 | 600 | 0.1068 | - | | 0.8312 | 650 | 0.0866 | - | | 0.8951 | 700 | 0.0656 | - | | 0.9591 | 750 | 0.0477 | - | | 1.0230 | 800 | 0.0377 | - | | 1.0870 | 850 | 0.0249 | - | | 1.1509 | 900 | 0.0144 | - | | 1.2148 | 950 | 0.0131 | - | | 1.2788 | 1000 | 0.0153 | - | | 1.3427 | 1050 | 0.012 | - | | 1.4066 | 1100 | 0.0104 | - | | 1.4706 | 1150 | 0.0102 | - | | 1.5345 | 1200 | 0.0079 | - | | 1.5985 | 1250 | 0.0039 | - | | 1.6624 | 1300 | 0.0026 | - | | 1.7263 | 1350 | 0.0015 | - | | 1.7903 | 1400 | 0.001 | - | | 1.8542 | 1450 | 0.0013 | - | | 1.9182 | 1500 | 0.0013 | - | | 1.9821 | 1550 | 0.001 | - | | 2.0460 | 1600 | 0.0009 | - | | 2.1100 | 1650 | 0.0012 | - | | 2.1739 | 1700 | 0.0007 | - | | 2.2379 | 1750 | 0.0009 | - | | 2.3018 | 1800 | 0.0009 | - | | 2.3657 | 1850 | 0.0007 | - | | 2.4297 | 1900 | 0.0011 | - | | 2.4936 | 1950 | 0.0008 | - | | 2.5575 | 2000 | 0.0015 | - | | 2.6215 | 2050 | 0.0028 | - | | 2.6854 | 2100 | 0.0032 | - | | 2.7494 | 2150 | 0.0019 | - | | 2.8133 | 2200 | 0.0017 | - | | 2.8772 | 2250 | 0.0008 | - | | 2.9412 | 2300 | 0.0019 | - | | 3.0051 | 2350 | 0.0016 | - | | 3.0691 | 2400 | 0.0018 | - | | 3.1330 | 2450 | 0.0013 | - | | 3.1969 | 2500 | 0.0007 | - | | 3.2609 | 2550 | 0.0006 | - | | 3.3248 | 2600 | 0.0009 | - | | 3.3887 | 2650 | 0.0016 | - | | 3.4527 | 2700 | 0.002 | - | | 3.5166 | 2750 | 0.0032 | - | | 3.5806 | 2800 | 0.0012 | - | | 3.6445 | 2850 | 0.0012 | - | | 3.7084 | 2900 | 0.0014 | - | | 3.7724 | 2950 | 0.0011 | - | | 3.8363 | 3000 | 0.0005 | - | | 3.9003 | 3050 | 0.0007 | - | | 3.9642 | 3100 | 0.0004 | - | | 4.0281 | 3150 | 0.0003 | - | | 4.0921 | 3200 | 0.0007 | - | | 4.1560 | 3250 | 0.0005 | - | | 4.2199 | 3300 | 0.0005 | - | | 4.2839 | 3350 | 0.0006 | - | | 4.3478 | 3400 | 0.0004 | - | | 4.4118 | 3450 | 0.0004 | - | | 4.4757 | 3500 | 0.0008 | - | | 4.5396 | 3550 | 0.0006 | - | | 4.6036 | 3600 | 0.0003 | - | | 4.6675 | 3650 | 0.0007 | - | | 4.7315 | 3700 | 0.0009 | - | | 4.7954 | 3750 | 0.0005 | - | | 4.8593 | 3800 | 0.0006 | - | | 4.9233 | 3850 | 0.0007 | - | | 4.9872 | 3900 | 0.0005 | - | | 5.0512 | 3950 | 0.0006 | - | | 5.1151 | 4000 | 0.0004 | - | | 5.1790 | 4050 | 0.0005 | - | | 5.2430 | 4100 | 0.0007 | - | | 5.3069 | 4150 | 0.0004 | - | | 5.3708 | 4200 | 0.0005 | - | | 5.4348 | 4250 | 0.0004 | - | | 5.4987 | 4300 | 0.0005 | - | | 5.5627 | 4350 | 0.0007 | - | | 5.6266 | 4400 | 0.0006 | - | | 5.6905 | 4450 | 0.0006 | - | | 5.7545 | 4500 | 0.0006 | - | | 5.8184 | 4550 | 0.0005 | - | | 5.8824 | 4600 | 0.0005 | - | | 5.9463 | 4650 | 0.0008 | - | | 6.0102 | 4700 | 0.0005 | - | | 6.0742 | 4750 | 0.0006 | - | | 6.1381 | 4800 | 0.0004 | - | | 6.2020 | 4850 | 0.0005 | - | | 6.2660 | 4900 | 0.0007 | - | | 6.3299 | 4950 | 0.0007 | - | | 6.3939 | 5000 | 0.0005 | - | | 6.4578 | 5050 | 0.0005 | - | | 6.5217 | 5100 | 0.0005 | - | | 6.5857 | 5150 | 0.0007 | - | | 6.6496 | 5200 | 0.0006 | - | | 6.7136 | 5250 | 0.0004 | - | | 6.7775 | 5300 | 0.0005 | - | | 6.8414 | 5350 | 0.0004 | - | | 6.9054 | 5400 | 0.0009 | - | | 6.9693 | 5450 | 0.0009 | - | | 7.0332 | 5500 | 0.0007 | - | | 7.0972 | 5550 | 0.0009 | - | | 7.1611 | 5600 | 0.0093 | - | | 7.2251 | 5650 | 0.0075 | - | | 7.2890 | 5700 | 0.0017 | - | | 7.3529 | 5750 | 0.0012 | - | | 7.4169 | 5800 | 0.001 | - | | 7.4808 | 5850 | 0.0008 | - | | 7.5448 | 5900 | 0.0005 | - | | 7.6087 | 5950 | 0.0005 | - | | 7.6726 | 6000 | 0.0006 | - | | 7.7366 | 6050 | 0.0007 | - | | 7.8005 | 6100 | 0.0006 | - | | 7.8645 | 6150 | 0.0006 | - | | 7.9284 | 6200 | 0.0004 | - | | 7.9923 | 6250 | 0.0006 | - | | 8.0563 | 6300 | 0.0004 | - | | 8.1202 | 6350 | 0.0005 | - | | 8.1841 | 6400 | 0.0005 | - | | 8.2481 | 6450 | 0.0006 | - | | 8.3120 | 6500 | 0.0005 | - | | 8.3760 | 6550 | 0.0006 | - | | 8.4399 | 6600 | 0.0004 | - | | 8.5038 | 6650 | 0.0007 | - | | 8.5678 | 6700 | 0.0006 | - | | 8.6317 | 6750 | 0.0004 | - | | 8.6957 | 6800 | 0.0005 | - | | 8.7596 | 6850 | 0.0009 | - | | 8.8235 | 6900 | 0.0006 | - | | 8.8875 | 6950 | 0.0007 | - | | 8.9514 | 7000 | 0.0007 | - | | 9.0153 | 7050 | 0.0003 | - | | 9.0793 | 7100 | 0.0006 | - | | 9.1432 | 7150 | 0.0007 | - | | 9.2072 | 7200 | 0.0008 | - | | 9.2711 | 7250 | 0.0004 | - | | 9.3350 | 7300 | 0.0006 | - | | 9.3990 | 7350 | 0.0005 | - | | 9.4629 | 7400 | 0.0006 | - | | 9.5269 | 7450 | 0.0006 | - | | 9.5908 | 7500 | 0.0005 | - | | 9.6547 | 7550 | 0.0006 | - | | 9.7187 | 7600 | 0.0005 | - | | 9.7826 | 7650 | 0.0006 | - | | 9.8465 | 7700 | 0.0006 | - | | 9.9105 | 7750 | 0.0006 | - | | 9.9744 | 7800 | 0.0007 | - | | 10.0384 | 7850 | 0.0018 | - | | 10.1023 | 7900 | 0.0045 | - | | 10.1662 | 7950 | 0.0024 | - | | 10.2302 | 8000 | 0.0013 | - | | 10.2941 | 8050 | 0.001 | - | | 10.3581 | 8100 | 0.0008 | - | | 10.4220 | 8150 | 0.0005 | - | | 10.4859 | 8200 | 0.0004 | - | | 10.5499 | 8250 | 0.0004 | - | | 10.6138 | 8300 | 0.0004 | - | | 10.6777 | 8350 | 0.0006 | - | | 10.7417 | 8400 | 0.0007 | - | | 10.8056 | 8450 | 0.0007 | - | | 10.8696 | 8500 | 0.0005 | - | | 10.9335 | 8550 | 0.0005 | - | | 10.9974 | 8600 | 0.0007 | - | | 11.0614 | 8650 | 0.0006 | - | | 11.1253 | 8700 | 0.0004 | - | | 11.1893 | 8750 | 0.0006 | - | | 11.2532 | 8800 | 0.0004 | - | | 11.3171 | 8850 | 0.0004 | - | | 11.3811 | 8900 | 0.0006 | - | | 11.4450 | 8950 | 0.0006 | - | | 11.5090 | 9000 | 0.0008 | - | | 11.5729 | 9050 | 0.0005 | - | | 11.6368 | 9100 | 0.0005 | - | | 11.7008 | 9150 | 0.0005 | - | | 11.7647 | 9200 | 0.0007 | - | | 11.8286 | 9250 | 0.0007 | - | | 11.8926 | 9300 | 0.0008 | - | | 11.9565 | 9350 | 0.0007 | - | | 12.0205 | 9400 | 0.0006 | - | | 12.0844 | 9450 | 0.0009 | - | | 12.1483 | 9500 | 0.0008 | - | | 12.2123 | 9550 | 0.0005 | - | | 12.2762 | 9600 | 0.0005 | - | | 12.3402 | 9650 | 0.0004 | - | | 12.4041 | 9700 | 0.0005 | - | | 12.4680 | 9750 | 0.0003 | - | | 12.5320 | 9800 | 0.0004 | - | | 12.5959 | 9850 | 0.0006 | - | | 12.6598 | 9900 | 0.0007 | - | | 12.7238 | 9950 | 0.0006 | - | | 12.7877 | 10000 | 0.0006 | - | | 12.8517 | 10050 | 0.0005 | - | | 12.9156 | 10100 | 0.0009 | - | | 12.9795 | 10150 | 0.0004 | - | | 13.0435 | 10200 | 0.0003 | - | | 13.1074 | 10250 | 0.0007 | - | | 13.1714 | 10300 | 0.0005 | - | | 13.2353 | 10350 | 0.001 | - | | 13.2992 | 10400 | 0.001 | - | | 13.3632 | 10450 | 0.0006 | - | | 13.4271 | 10500 | 0.0006 | - | | 13.4910 | 10550 | 0.0007 | - | | 13.5550 | 10600 | 0.0005 | - | | 13.6189 | 10650 | 0.0004 | - | | 13.6829 | 10700 | 0.0006 | - | | 13.7468 | 10750 | 0.0005 | - | | 13.8107 | 10800 | 0.0006 | - | | 13.8747 | 10850 | 0.0005 | - | | 13.9386 | 10900 | 0.0007 | - | | 14.0026 | 10950 | 0.0005 | - | | 14.0665 | 11000 | 0.0004 | - | | 14.1304 | 11050 | 0.0005 | - | | 14.1944 | 11100 | 0.0006 | - | | 14.2583 | 11150 | 0.0004 | - | | 14.3223 | 11200 | 0.0006 | - | | 14.3862 | 11250 | 0.0006 | - | | 14.4501 | 11300 | 0.0005 | - | | 14.5141 | 11350 | 0.0008 | - | | 14.5780 | 11400 | 0.0007 | - | | 14.6419 | 11450 | 0.0005 | - | | 14.7059 | 11500 | 0.0005 | - | | 14.7698 | 11550 | 0.0007 | - | | 14.8338 | 11600 | 0.0004 | - | | 14.8977 | 11650 | 0.0005 | - | | 14.9616 | 11700 | 0.0007 | - | | 15.0256 | 11750 | 0.0007 | - | | 15.0895 | 11800 | 0.0006 | - | | 15.1535 | 11850 | 0.0005 | - | | 15.2174 | 11900 | 0.0002 | - | | 15.2813 | 11950 | 0.0006 | - | | 15.3453 | 12000 | 0.0006 | - | | 15.4092 | 12050 | 0.0004 | - | | 15.4731 | 12100 | 0.0005 | - | | 15.5371 | 12150 | 0.0038 | - | | 15.6010 | 12200 | 0.0088 | - | | 15.6650 | 12250 | 0.001 | - | | 15.7289 | 12300 | 0.0005 | - | | 15.7928 | 12350 | 0.0007 | - | | 15.8568 | 12400 | 0.0005 | - | | 15.9207 | 12450 | 0.0005 | - | | 15.9847 | 12500 | 0.0006 | - | | 16.0486 | 12550 | 0.0012 | - | | 16.1125 | 12600 | 0.0009 | - | | 16.1765 | 12650 | 0.0029 | - | | 16.2404 | 12700 | 0.0006 | - | | 16.3043 | 12750 | 0.0007 | - | | 16.3683 | 12800 | 0.0006 | - | | 16.4322 | 12850 | 0.0007 | - | | 16.4962 | 12900 | 0.0006 | - | | 16.5601 | 12950 | 0.0006 | - | | 16.6240 | 13000 | 0.0006 | - | | 16.6880 | 13050 | 0.0007 | - | | 16.7519 | 13100 | 0.0004 | - | | 16.8159 | 13150 | 0.0004 | - | | 16.8798 | 13200 | 0.0004 | - | | 16.9437 | 13250 | 0.0007 | - | | 17.0077 | 13300 | 0.0004 | - | | 17.0716 | 13350 | 0.0004 | - | | 17.1355 | 13400 | 0.0005 | - | | 17.1995 | 13450 | 0.0005 | - | | 17.2634 | 13500 | 0.0007 | - | | 17.3274 | 13550 | 0.0004 | - | | 17.3913 | 13600 | 0.0008 | - | | 17.4552 | 13650 | 0.0004 | - | | 17.5192 | 13700 | 0.0009 | - | | 17.5831 | 13750 | 0.0003 | - | | 17.6471 | 13800 | 0.0005 | - | | 17.7110 | 13850 | 0.0007 | - | | 17.7749 | 13900 | 0.0007 | - | | 17.8389 | 13950 | 0.0007 | - | | 17.9028 | 14000 | 0.0003 | - | | 17.9668 | 14050 | 0.0006 | - | | 18.0307 | 14100 | 0.0005 | - | | 18.0946 | 14150 | 0.0006 | - | | 18.1586 | 14200 | 0.0005 | - | | 18.2225 | 14250 | 0.0004 | - | | 18.2864 | 14300 | 0.0005 | - | | 18.3504 | 14350 | 0.0006 | - | | 18.4143 | 14400 | 0.0006 | - | | 18.4783 | 14450 | 0.0006 | - | | 18.5422 | 14500 | 0.0006 | - | | 18.6061 | 14550 | 0.0005 | - | | 18.6701 | 14600 | 0.0005 | - | | 18.7340 | 14650 | 0.0004 | - | | 18.7980 | 14700 | 0.0006 | - | | 18.8619 | 14750 | 0.0005 | - | | 18.9258 | 14800 | 0.0007 | - | | 18.9898 | 14850 | 0.0005 | - | | 19.0537 | 14900 | 0.0003 | - | | 19.1176 | 14950 | 0.0002 | - | | 19.1816 | 15000 | 0.0005 | - | | 19.2455 | 15050 | 0.0005 | - | | 19.3095 | 15100 | 0.0005 | - | | 19.3734 | 15150 | 0.0004 | - | | 19.4373 | 15200 | 0.0007 | - | | 19.5013 | 15250 | 0.0006 | - | | 19.5652 | 15300 | 0.0005 | - | | 19.6292 | 15350 | 0.0005 | - | | 19.6931 | 15400 | 0.0004 | - | | 19.7570 | 15450 | 0.0006 | - | | 19.8210 | 15500 | 0.0005 | - | | 19.8849 | 15550 | 0.001 | - | | 19.9488 | 15600 | 0.002 | - | | 20.0128 | 15650 | 0.0016 | - | | 20.0767 | 15700 | 0.0011 | - | | 20.1407 | 15750 | 0.0005 | - | | 20.2046 | 15800 | 0.0007 | - | | 20.2685 | 15850 | 0.0009 | - | | 20.3325 | 15900 | 0.0004 | - | | 20.3964 | 15950 | 0.0004 | - | | 20.4604 | 16000 | 0.0005 | - | | 20.5243 | 16050 | 0.0004 | - | | 20.5882 | 16100 | 0.0007 | - | | 20.6522 | 16150 | 0.0006 | - | | 20.7161 | 16200 | 0.0006 | - | | 20.7801 | 16250 | 0.0004 | - | | 20.8440 | 16300 | 0.0004 | - | | 20.9079 | 16350 | 0.0007 | - | | 20.9719 | 16400 | 0.0006 | - | | 21.0358 | 16450 | 0.0005 | - | | 21.0997 | 16500 | 0.0006 | - | | 21.1637 | 16550 | 0.0007 | - | | 21.2276 | 16600 | 0.0004 | - | | 21.2916 | 16650 | 0.0003 | - | | 21.3555 | 16700 | 0.0003 | - | | 21.4194 | 16750 | 0.0005 | - | | 21.4834 | 16800 | 0.0006 | - | | 21.5473 | 16850 | 0.0007 | - | | 21.6113 | 16900 | 0.0006 | - | | 21.6752 | 16950 | 0.0003 | - | | 21.7391 | 17000 | 0.0007 | - | | 21.8031 | 17050 | 0.0003 | - | | 21.8670 | 17100 | 0.0006 | - | | 21.9309 | 17150 | 0.0007 | - | | 21.9949 | 17200 | 0.0007 | - | | 22.0588 | 17250 | 0.0008 | - | | 22.1228 | 17300 | 0.0007 | - | | 22.1867 | 17350 | 0.0007 | - | | 22.2506 | 17400 | 0.0004 | - | | 22.3146 | 17450 | 0.0004 | - | | 22.3785 | 17500 | 0.0003 | - | | 22.4425 | 17550 | 0.0006 | - | | 22.5064 | 17600 | 0.0007 | - | | 22.5703 | 17650 | 0.0006 | - | | 22.6343 | 17700 | 0.0004 | - | | 22.6982 | 17750 | 0.0006 | - | | 22.7621 | 17800 | 0.0006 | - | | 22.8261 | 17850 | 0.0006 | - | | 22.8900 | 17900 | 0.0004 | - | | 22.9540 | 17950 | 0.0006 | - | | 23.0179 | 18000 | 0.0005 | - | | 23.0818 | 18050 | 0.0003 | - | | 23.1458 | 18100 | 0.0006 | - | | 23.2097 | 18150 | 0.0006 | - | | 23.2737 | 18200 | 0.0006 | - | | 23.3376 | 18250 | 0.0007 | - | | 23.4015 | 18300 | 0.0005 | - | | 23.4655 | 18350 | 0.0005 | - | | 23.5294 | 18400 | 0.0008 | - | | 23.5934 | 18450 | 0.0004 | - | | 23.6573 | 18500 | 0.0006 | - | | 23.7212 | 18550 | 0.0004 | - | | 23.7852 | 18600 | 0.0006 | - | | 23.8491 | 18650 | 0.0007 | - | | 23.9130 | 18700 | 0.0006 | - | | 23.9770 | 18750 | 0.0006 | - | | 24.0409 | 18800 | 0.0005 | - | | 24.1049 | 18850 | 0.0002 | - | | 24.1688 | 18900 | 0.0006 | - | | 24.2327 | 18950 | 0.0005 | - | | 24.2967 | 19000 | 0.0004 | - | | 24.3606 | 19050 | 0.0006 | - | | 24.4246 | 19100 | 0.0006 | - | | 24.4885 | 19150 | 0.0007 | - | | 24.5524 | 19200 | 0.0007 | - | | 24.6164 | 19250 | 0.0005 | - | | 24.6803 | 19300 | 0.0004 | - | | 24.7442 | 19350 | 0.0006 | - | | 24.8082 | 19400 | 0.0005 | - | | 24.8721 | 19450 | 0.0007 | - | | 24.9361 | 19500 | 0.0007 | - | | 25.0 | 19550 | 0.0006 | - | | 25.0639 | 19600 | 0.0005 | - | | 25.1279 | 19650 | 0.0007 | - | | 25.1918 | 19700 | 0.0006 | - | | 25.2558 | 19750 | 0.0005 | - | | 25.3197 | 19800 | 0.0005 | - | | 25.3836 | 19850 | 0.0006 | - | | 25.4476 | 19900 | 0.0008 | - | | 25.5115 | 19950 | 0.0006 | - | | 25.5754 | 20000 | 0.0003 | - | | 25.6394 | 20050 | 0.0007 | - | | 25.7033 | 20100 | 0.0006 | - | | 25.7673 | 20150 | 0.0004 | - | | 25.8312 | 20200 | 0.0005 | - | | 25.8951 | 20250 | 0.0007 | - | | 25.9591 | 20300 | 0.0004 | - | | 26.0230 | 20350 | 0.0006 | - | | 26.0870 | 20400 | 0.0007 | - | | 26.1509 | 20450 | 0.0004 | - | | 26.2148 | 20500 | 0.0006 | - | | 26.2788 | 20550 | 0.0006 | - | | 26.3427 | 20600 | 0.0004 | - | | 26.4066 | 20650 | 0.0006 | - | | 26.4706 | 20700 | 0.0006 | - | | 26.5345 | 20750 | 0.0005 | - | | 26.5985 | 20800 | 0.0008 | - | | 26.6624 | 20850 | 0.0005 | - | | 26.7263 | 20900 | 0.0008 | - | | 26.7903 | 20950 | 0.0003 | - | | 26.8542 | 21000 | 0.0006 | - | | 26.9182 | 21050 | 0.0004 | - | | 26.9821 | 21100 | 0.0003 | - | | 27.0460 | 21150 | 0.0005 | - | | 27.1100 | 21200 | 0.0007 | - | | 27.1739 | 21250 | 0.0007 | - | | 27.2379 | 21300 | 0.0003 | - | | 27.3018 | 21350 | 0.0005 | - | | 27.3657 | 21400 | 0.0007 | - | | 27.4297 | 21450 | 0.0006 | - | | 27.4936 | 21500 | 0.0005 | - | | 27.5575 | 21550 | 0.0004 | - | | 27.6215 | 21600 | 0.0008 | - | | 27.6854 | 21650 | 0.0005 | - | | 27.7494 | 21700 | 0.0006 | - | | 27.8133 | 21750 | 0.0004 | - | | 27.8772 | 21800 | 0.0004 | - | | 27.9412 | 21850 | 0.0005 | - | | 28.0051 | 21900 | 0.0007 | - | | 28.0691 | 21950 | 0.0006 | - | | 28.1330 | 22000 | 0.0008 | - | | 28.1969 | 22050 | 0.0008 | - | | 28.2609 | 22100 | 0.0003 | - | | 28.3248 | 22150 | 0.0005 | - | | 28.3887 | 22200 | 0.0005 | - | | 28.4527 | 22250 | 0.0005 | - | | 28.5166 | 22300 | 0.0009 | - | | 28.5806 | 22350 | 0.0004 | - | | 28.6445 | 22400 | 0.0007 | - | | 28.7084 | 22450 | 0.0004 | - | | 28.7724 | 22500 | 0.0004 | - | | 28.8363 | 22550 | 0.0004 | - | | 28.9003 | 22600 | 0.0003 | - | | 28.9642 | 22650 | 0.0005 | - | | 29.0281 | 22700 | 0.0007 | - | | 29.0921 | 22750 | 0.0005 | - | | 29.1560 | 22800 | 0.0004 | - | | 29.2199 | 22850 | 0.0005 | - | | 29.2839 | 22900 | 0.0007 | - | | 29.3478 | 22950 | 0.0005 | - | | 29.4118 | 23000 | 0.0004 | - | | 29.4757 | 23050 | 0.0006 | - | | 29.5396 | 23100 | 0.0004 | - | | 29.6036 | 23150 | 0.0006 | - | | 29.6675 | 23200 | 0.0005 | - | | 29.7315 | 23250 | 0.0005 | - | | 29.7954 | 23300 | 0.0007 | - | | 29.8593 | 23350 | 0.0006 | - | | 29.9233 | 23400 | 0.0006 | - | | 29.9872 | 23450 | 0.0006 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.3.1 - Transformers: 4.44.2 - PyTorch: 2.2.0a0+81ea7a4 - Datasets: 3.2.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```